Did Spotify users in the Netherlands change their listening behavior during the COVID-19 pandemic?
Computational Musicology Portfolio
For the assignment for the course ‘Computational Musicology’ a portfolio will be created in order to perform various analyses relating to music. This portfolio will mainly focus on music listening behavior of Spotify in the Netherlands before and during the COVID-19 pandemic. Specifically, whether music preferences have shifted during the pandemic and whether changes can be identified to the restrictions imposed by the Dutch government (e.g. lockdown and curfew).
Corpus
In order to analyze general listening behavior, the corpus will be focused on the following variables:
- Playlist
- Spotify Audio Features
- Time
Playlist
In order to keep track on the average listening behavior of Dutch Spotify users, the weekly ‘Top 50’ and ‘Viral 50’ playlists from the Netherlands will be analyzed over time.
Spotify Audio Features
The changes (or lack thereof) listening behavior will be measured by the the different Audio Features from the Spotify API.
Time
The variable time will be used to identify periods before and during the pandemic that may explain the change of musical preferences as shown in the top and viral playlists. As well as to help compare annual periods such as the December Holiday season before and during the pandemic.
Trip down memory lane: What was life like before the pandemic?
Some text
Covid-19: Visualized
# coeff <- 10
#
# # A few constants
# temperatureColor <- "#69b3a2"
# priceColor <- rgb(0.2, 0.6, 0.9, 1)
#
# ggplot(head(data, 80), aes(x=day)) +
#
# geom_bar( aes(y=temperature), stat="identity", size=.1, fill=temperatureColor, color="black", alpha=.4) +
# geom_line( aes(y=price / coeff), size=2, color=priceColor) +
#
# scale_y_continuous(
#
# # Features of the first axis
# name = "Temperature (Celsius °)",
#
# # Add a second axis and specify its features
# sec.axis = sec_axis(~.*coeff, name="Price ($)")
# ) +
#
# theme_ipsum() +
#
# theme(
# axis.title.y = element_text(color = temperatureColor, size=13),
# axis.title.y.right = element_text(color = priceColor, size=13)
# ) +
#
# ggtitle("Temperature down, price up")The first case
On February 27th, 2020 (week 9), the first case of COVID-19 had been confirmed in the Netherlands. Before this occurrence, when everything was normal, the number of streams of the top songs decreased until the end of the holiday season. Probably due people coming together to enjoy (the holiday) music, movies or other activities together, instead of individually. In the first few weeks of 2021 the number of streams started rising again.
From week 5 the number of top streams started to decline. And even When the first cases, the first admissions into the hospitals, and first deaths were reported the number continued to decline.
Togetherness and solidariy during lockdown
As the situation became more severe, with record COVID-related hostpital admissions in week 13, the Dutch government implemented the ‘intelligent’ lockdown. This led to a relative high public solidarity towards those affected by the virus, especially health care workers. This may explain the sudden spike of the top stream from 1,598,458 to 3,482,822 streams in two weeks, with the song 17 Miljoen Mensen - Live @538 in Ahoy - Davina Michelle topping the charts for five consecutive weeks. 17 Miljoen Mensen (17 Million people) was dedicated to the Dutch people who are affected by the virus.
Early Christmas: All I Want before Christmas… Is Christmas
Christmas songs started to dominate the charts in 2020 around week 49 until week 53, whereas in 2019 Christmas this phenomenon occurred much later. In 2020 it is noticable that the bottom corner with tracks with relatively high BPM, high valence, and low energy are from the weeks when Christmas tracks dominated the charts. In 2019 this noticeable in week 52 and to a lesser extent weeks 50 and 51.
17 Miljoen Mensen - 15 Miljoen Mensen
“17 Miljoen Mensen” (2021) is actually a cover of the track “15 Miljoen Mensen” (1996), we’ll analyze if there are any similarites (or lack thereof) between the two tracks. One instantly noticed difference is the adjustment in the track’s title for the population increase of 2 million people.
The Chromagram and table below show that there significant differences between the two tracks.
| V1 | V1 | |
|---|---|---|
| danceability | 0.493 | 0.547 |
| energy | 0.321 | 0.631 |
| key | 7 | 0 |
| loudness | -10.041 | -7.063 |
| mode | 1 | 1 |
| speechiness | 0.0402 | 0.0266 |
| acousticness | 0.715 | 0.0943 |
| instrumentalness | 0 | 0 |
| liveness | 0.0863 | 0.0548 |
| valence | 0.508 | 0.481 |
| tempo | 86.77 | 79.02 |
| type | audio_features | audio_features |
| id | 7e42rjxCt8tPjglU9VyBcz | 2GBJFvDr62eIX24a3t6pBr |
| uri | spotify:track:7e42rjxCt8tPjglU9VyBcz | spotify:track:2GBJFvDr62eIX24a3t6pBr |
| track_href | https://api.spotify.com/v1/tracks/7e42rjxCt8tPjglU9VyBcz | https://api.spotify.com/v1/tracks/2GBJFvDr62eIX24a3t6pBr |
| analysis_url | https://api.spotify.com/v1/audio-analysis/7e42rjxCt8tPjglU9VyBcz | https://api.spotify.com/v1/audio-analysis/2GBJFvDr62eIX24a3t6pBr |
| duration_ms | 107200 | 236107 |
| time_signature | 4 | 4 |
Frame 6
Don’t know